CN111817296A - Electric energy scheduling method and system for micro-grid - Google Patents

Electric energy scheduling method and system for micro-grid Download PDF

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CN111817296A
CN111817296A CN202010653703.XA CN202010653703A CN111817296A CN 111817296 A CN111817296 A CN 111817296A CN 202010653703 A CN202010653703 A CN 202010653703A CN 111817296 A CN111817296 A CN 111817296A
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microgrid
model
battery
power
power distribution
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CN111817296B (en
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白保华
沈欣炜
彭勇
吴逸倩
殷仁鹏
夏天
蒋剑峰
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State Grid Comprehensive Energy Service Group Co ltd
Tsinghua University
State Grid Corp of China SGCC
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State Grid Comprehensive Energy Service Group Co ltd
Tsinghua University
State Grid Corp of China SGCC
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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Abstract

The invention provides an electric energy scheduling method and system for a micro-grid, wherein the method comprises the following steps: acquiring historical power distribution information and a power distribution plan of the microgrid equipment; based on historical power distribution information and a power distribution plan, and with a preset time period as a cycle, optimizing a preset decision model function through a rolling optimization algorithm for model prediction control; solving the optimized preset decision model function; and scheduling the power distribution plan of the microgrid equipment according to the solving result. Through the technical scheme, the method can realize that: the method comprises the steps of constructing corresponding models according to the operating characteristics of different microgrid devices in the microgrid, introducing a rolling optimization algorithm for model predictive control strategy design optimization, designing utility functions containing microgrid device use preference and device safe operating conditions, and achieving the aims of reducing peak load in the microgrid, stabilizing power requirements, guaranteeing operation safety of the microgrid and saving power generation cost through electric energy transaction, scheduling and collaborative management among users.

Description

Electric energy scheduling method and system for micro-grid
Technical Field
The invention belongs to the field of comprehensive energy systems, and particularly relates to a method and a system for scheduling electric energy for a microgrid.
Background
Distributed renewable energy and other emerging technologies are continuously developed, novel power grid structures such as 'micro-grid' are continuously popularized, and a series of technical innovations provide new opportunities for structural innovation of power systems. Under the ordered promotion of the innovation of the power system, the market of the power selling side is gradually opened, and a plurality of bodies such as distributed power supplies are allowed to participate in market competition. More and more consumers of electricity, defined as "passive consumers" in the traditional electricity market, will become consumers of electricity and consumers of electricity to participate in electricity market transactions, consuming, producing and storing electricity and energy. Therefore, finding a flexible resource scheduling mechanism and an efficient electric energy transaction system is increasingly receiving wide attention of industrial entities and researchers.
The decentralization of energy production, exemplified by distributed energy, has prompted energy market trading to develop towards deployment. The current research finds that the electric energy point-to-point trading mode is mainly suitable for the electric power market environment with easily fluctuating demand, diversified load and smaller scale. In such markets, the peer-to-peer trading mode of electrical energy may rely on a corresponding incentive mechanism to facilitate coordination of usage patterns of distributed energy and other load devices among users, during which users may participate in trading, the formulation of service terms, and trade with each other on the basis thereof, enabling users with excess electrical energy and users with complementary needs to achieve a mutually profitable energy trade, thereby attenuating the effects of load uncertainty of distributed energy and other load devices, and further reducing upstream power generation and transportation requirements, thereby reducing overall trading costs. However, in the prior art, the specific design of electric energy scheduling cooperation of users in the micro-grid level is still less, and promotion of user transaction cooperation and reduction of the influence of load uncertainty cannot be realized, so that the operation safety of the power grid is guaranteed, and the operation cost of the power grid is reduced.
Disclosure of Invention
In view of the above problems, the present invention provides a method and a system for scheduling electric energy for a microgrid, which overcome the above technical problems. The method comprises the following steps:
in order to achieve the above object, a first aspect of the present application discloses a method for scheduling electric energy for a microgrid, the method comprising: acquiring historical power distribution information and a power distribution plan of the microgrid equipment; based on the historical power distribution information and the power distribution plan, and with a preset time period as a cycle, optimizing a preset decision model function through a rolling optimization algorithm for model prediction control; solving the optimized preset decision model function; and scheduling the power distribution plan of the microgrid equipment according to the solving result.
Optionally, the solving the optimized preset decision model function includes: decomposing the optimized preset decision model function into a power distribution model function corresponding to the microgrid equipment by using a dual decomposition method; and based on the constraint model of the micro-grid equipment, performing distributed solution on the power distribution model function corresponding to the micro-grid equipment, and coordinating by using a gradient iterative algorithm to obtain an optimal solution, wherein the optimal solution is the solution result.
Optionally, the constraint model of the microgrid device includes one or more of:
a fixed load model, a dispatchable load model, an interruptible load model, a battery model, or a renewable energy model.
Alternatively to this, the first and second parts may,
the constraint equation of the fixed load model comprises:
Figure BDA0002575947280000021
wherein p isi,a(t) denotes that within a certain time slot t, the building is built
Figure BDA0002575947280000022
Internal device
Figure BDA0002575947280000023
The required power;
Figure BDA0002575947280000024
represents a collection of buildings inside the microgrid,
Figure BDA0002575947280000025
represents a collection of devices inside a building i,
Figure BDA0002575947280000026
indicating the period of time the device is operating.
Optionally, the constraint equation of the schedulable load model includes:
Figure BDA0002575947280000027
Figure BDA0002575947280000028
wherein the content of the first and second substances,
Figure BDA0002575947280000029
representing the minimum power at which device a operates in a certain time slot t,
Figure BDA00025759472800000210
represents the maximum power at which the device a operates in a certain time slot t,
Figure BDA00025759472800000211
indicating the minimum total energy consumption required by device a during the operating period,
Figure BDA00025759472800000212
the maximum total energy consumption required by the equipment a in the operation time period is shown, and delta T represents the time interval between adjacent sampling points;
the utility function of the schedulable load model is represented as:
Figure BDA00025759472800000213
namely:
Figure BDA00025759472800000214
wherein, ci,a、bi,aAll are positive constants and represent utility calculation coefficients of the equipment a.
Optionally, the constraint equation of the interruptible load model includes:
Figure BDA0002575947280000031
the utility function of the interruptible load model comprises:
Figure BDA0002575947280000032
namely, it is
Figure BDA0002575947280000033
Optionally, the dynamic change of the energy of the battery model includes:
Figure BDA0002575947280000034
wherein, bi,a(t) represents the battery energy level in time slot t, bi,a(t-1) represents the battery energy level during time slot t-1,
Figure BDA0002575947280000035
representing the battery charging power during the time slot t,
Figure BDA0002575947280000036
representing the battery discharge power, alpha, in time slot ts∈(0,1]The rate of loss of storage of the battery is expressed,
Figure BDA0002575947280000037
which represents the efficiency of the charging of the battery,
Figure BDA0002575947280000038
represents the battery discharge efficiency, and ηs∈(0,1]。
The constraint formula of the battery model comprises:
Figure BDA0002575947280000039
Figure BDA00025759472800000310
Figure BDA00025759472800000311
wherein, Bi,aThe capacity of the battery is represented by,
Figure BDA00025759472800000312
indicates the minimum charging power of the battery,
Figure BDA00025759472800000313
which represents the maximum charging power of the battery,
Figure BDA00025759472800000314
indicates the minimum discharge power of the battery,
Figure BDA00025759472800000315
representing the maximum discharge power of the battery.
Optionally, the constraint formula of the renewable energy model includes:
Figure BDA00025759472800000316
moreover, the utility function of the renewable energy model includes:
Ui,a(pi,a(t),t)＝ci,a-bi,api,a(t)
optionally, the preset decision model function includes:
Figure BDA0002575947280000041
Figure BDA0002575947280000042
wherein x represents a feasible domain satisfying the operation requirements of each micro-grid device,
Figure BDA0002575947280000043
representing the prediction period, P (t) representing the total real-time power consumption during the time slot t, PnomAnd (t) the planned power consumption in the day ahead, wherein alpha and beta are coefficients which represent the balance of minimizing the deviation of the actual power consumption and the planned rated power consumption and maximizing the utility of the user in a decision function respectively.
A second aspect of the present application discloses an electric energy scheduling system for a microgrid, the system comprising:
the acquisition module is used for acquiring historical power distribution information and a power distribution plan of the microgrid equipment;
the optimization module is used for optimizing a preset decision model function through a rolling optimization algorithm for model prediction control based on the historical power distribution information and with a preset time period as a cycle;
the solving module is used for solving the optimized preset decision model function;
and the scheduling module is used for scheduling the power distribution plan of the micro-grid equipment according to the solving result.
The invention provides an electric energy scheduling method and system for a microgrid aiming at microgrid-level users, wherein corresponding models are built according to the operating characteristics of different microgrid devices in the microgrid, a rolling optimization algorithm for model predictive control strategy design optimization is introduced, utility functions including microgrid device use preference and device safe operating conditions are designed, and the aims of reducing peak load in the microgrid, stabilizing electric power requirements, guaranteeing operation safety of the microgrid and saving power generation cost are achieved through electric energy transaction, scheduling and cooperative management among users.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flow chart of a method for scheduling electric energy for a microgrid according to an embodiment of the present invention;
fig. 2 shows a schematic structural diagram of an electric energy dispatching system for a microgrid according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In this embodiment, the internal control architecture of the microgrid system is as follows: a central energy management system is arranged at a node of the micro-grid and the upper network, and is used as an intermediary for communication and transaction between the micro-grid and the upper network, and coordinates activities such as information exchange and electric energy transaction among building users in the micro-grid; meanwhile, the user side takes buildings as units, a building energy management system is arranged in each building, the load dynamics of the micro-grid equipment in the building is collected, the electric energy generation and consumption conditions in the next scheduling time interval are estimated, and the operation state of the micro-grid equipment is controlled through bidirectional communication with a superior central energy management system. Different types of loads or equipment are arranged in each building, including energy storage equipment represented by a battery and distributed energy sources such as solar photovoltaic power generation and wind turbine power generation, and communication is performed between the buildings based on users under the condition of realizing individual benefit maximization and system benefit maximization, such as sharing distributed energy equipment.
Moreover, the following power balance conditions should be satisfied in the operation process of the electric energy transaction model between the micro-grid devices:
Figure BDA0002575947280000051
wherein, P (t) represents that the upper network in the time slot t supplies the power to the micro-grid if the building in the micro-grid
Figure BDA0002575947280000052
Micro-grid device
Figure BDA0002575947280000053
All run at rated working conditionsThen P (t) is equal to Pnom(t) the real-time total power consumption P (t) is equal to the planned rated power consumption P before a period of timenom(t) of (d). For the battery
Figure BDA0002575947280000054
pi,a(t) represents the electrical power of the building i battery during time slot t,
Figure BDA0002575947280000055
for renewable energy source equipment
Figure BDA0002575947280000056
pi,a(t) represents the electrical power from the renewable energy microgrid facility from building i within time slot t, pi,a(t)<0 represents the new energy source supplying power to the building; for load equipment
Figure BDA0002575947280000057
pi,a(t) represents the power required by the load device of building i in time slot t, if pi,a(t)>0, the equipment in the building consumes power.
In order to ensure the safe and stable operation of the power grid, the central energy management system coordinates the activities of information exchange, electric energy transaction and the like among buildings in the micro-grid to ensure that the real-time total power consumption P (t) in the micro-grid approaches to the planned rated power consumption Pnom(t) of (d). Moreover, the margin of the power consumption
Figure BDA0002575947280000061
Representing the margin by which the actual power usage deviates from the planned rated power usage. If the electric energy is equal to 0, the electric energy transaction generated between the node and the power grid is strictly performed according to expectation, and the load fluctuation in the power grid is completely adjusted by the node or the point-to-point electric energy transaction between buildings is responded; if the power consumption is too large, the deviation of the actual power consumption from the expected power consumption is far, the planned power consumption does not accord with the design target, and the operation safety of the power grid cannot be effectively guaranteed.
Fig. 1 shows a flow chart of an electric energy scheduling method for a microgrid according to an embodiment of the present invention. As shown in fig. 1, the aim is to reduce peak load in the microgrid, stabilize power demand, guarantee the operation safety of the microgrid and save power generation cost. The embodiment of the invention provides an electric energy scheduling method for a micro-grid, wherein the method can be exemplarily applied to a central energy management system. The method comprises the following steps:
acquiring historical power distribution information and a power distribution plan of the microgrid equipment; based on the historical power distribution information, and with a preset time period as a period, optimizing a preset decision model function through a rolling optimization algorithm for model prediction control; solving the optimized preset decision model function; and scheduling the power distribution plan of the microgrid equipment according to the solving result.
In this regard, a load model is constructed according to the operation characteristics of different microgrid devices in the microgrid, a rolling optimization algorithm for model prediction control strategy design optimization is introduced, a utility function containing microgrid device use preference and device safe operation conditions is designed, and the aims of reducing peak load in the microgrid, stabilizing power demand, guaranteeing operation safety of the microgrid and saving power generation cost are achieved through electric energy transaction, scheduling and collaborative management among users.
Specifically, the method for scheduling electric energy for a microgrid provided by the embodiment of the present invention includes:
s11, acquiring historical power distribution information and a power distribution plan of the microgrid equipment; such as: the central energy management system obtains respective historical power distribution information and power distribution plans of fixed loads, schedulable loads, interruptible loads, batteries or renewable energy sources in the microgrid system. Specifically, the power consumption of the washing machine in a previous preset time period is obtained, and the planned power consumption of the washing machine in a future preset time period is obtained.
Of course, the microgrid device referred to in the present embodiment is at least one device in the microgrid system.
S12, based on the historical power distribution information and the power distribution plan, and with a preset time period as a period, optimizing a preset decision model function through a rolling optimization algorithm for model prediction control;
specifically, the model predictive control is a closed-loop optimization control strategy based on a model, and in a prediction period
Figure BDA0002575947280000062
And according to the predicted system state parameters, adopting a short-term decision to calculate the optimal control input, applying the first value of the obtained control input sequence as an input value to the controlled object, and discarding the rest of the input sequence. At the next sampling instant, the above process is repeated: and (4) refreshing the optimization problem and solving again by using the state measurement value at the moment as an initial condition for predicting the future state variable of the system model at the moment, namely the first value of the state sequence. By the rolling optimization strategy, feedback control is realized, the system responds to system interference in real time, dynamic control of the system is realized, and prediction errors are compensated.
S13, solving the optimized preset decision model function; specifically, the optimized preset decision model function is solved, that is,: the optimization problem is solved, and typical solving algorithms comprise an original-dual method, a gradient method and the like.
Specifically, one solution includes: decomposing the optimized preset decision model function into a power distribution model function corresponding to the microgrid equipment by using a dual decomposition method; and based on the constraint model of the micro-grid equipment, performing distributed solution on the power distribution model function corresponding to the micro-grid equipment, and coordinating by using a gradient iterative algorithm to obtain an optimal solution, wherein the optimal solution is the solution result.
Constraint models for the microgrid devices include, but are not limited to, one or more of: a fixed load model, a dispatchable load model, an interruptible load model, a battery model, or a renewable energy model.
Of course, in this embodiment, the distribution model function corresponding to the microgrid device may be an existing distribution model function of each microgrid device, or if any microgrid device in the microgrid system is not configured with a corresponding distribution model function, the distribution model function corresponding to the microgrid device may also be determined before step S13.
And S14, scheduling the power distribution plan of the microgrid equipment according to the solution result.
The method is characterized in that a micro-grid device with fixed load is generally used for guaranteeing basic requirements of users, so that a building energy management system generally does not control the micro-grid device to adjust or reduce the load of the device, namely, the running time, running state and the like of the micro-grid device in the using process strictly accord with power utilization planning, including main lighting and the like. The constraint formula of the fixed load model corresponding to the micro-grid device comprises the following steps:
Figure BDA0002575947280000071
wherein p isi,a(t) denotes that within a certain time slot t, the building is built
Figure BDA0002575947280000072
Internal device
Figure BDA0002575947280000073
The required power;
Figure BDA0002575947280000074
represents a collection of buildings inside the microgrid,
Figure BDA0002575947280000075
represents a collection of devices inside a building i,
Figure BDA0002575947280000076
indicating the period of time the device is operating.
For micro-grid equipment with a load capable of being scheduled, which is also called a delay load, during the use process of such micro-grid equipment, the building energy management system can control the equipment to be unloaded or started or stopped, but the power of the micro-grid equipment needs to be kept higher than a certain minimum power consumption power to maintain the working state until the task is finished, generally, a user cares whether the micro-grid equipment can complete the power consumption task within a given time, that is, the accumulated power consumption of such micro-grid equipment must exceed a threshold value before the cutoff time, and the micro-grid equipment comprises a washing machine, a cooking device and the like. The constraint formula of the schedulable load model corresponding to the micro-grid device comprises the following steps:
Figure BDA0002575947280000081
Figure BDA0002575947280000082
wherein the content of the first and second substances,
Figure BDA0002575947280000083
representing the minimum power at which device a operates in a certain time slot t,
Figure BDA0002575947280000084
represents the maximum power at which the device a operates in a certain time slot t,
Figure BDA0002575947280000085
indicating the minimum total energy consumption required by device a during the operating period,
Figure BDA0002575947280000086
the maximum total energy consumption required by the equipment a in the operation time period is shown, and delta T represents the time interval between adjacent sampling points;
the utility function of the schedulable load model is represented as:
Figure BDA0002575947280000087
namely:
Figure BDA0002575947280000088
wherein, ci,a、bi,aAll are positive constants and represent utility calculation coefficients of the equipment a.
Aiming at the micro-grid equipment capable of interrupting load, the building energy management system can control the load shedding or the start and stop of the corresponding micro-grid equipment in the using process, such as secondary lighting, plug load, temperature control equipment and the like. Based on user comfort, satisfaction, etc., such microgrid devices often have user-preferred operating parameter values, which can generally be set by the user himself. Generally, the constraint formula of the interruptible load model corresponding to the microgrid device comprises:
Figure BDA0002575947280000089
the utility function of the interruptible load model comprises:
Figure BDA00025759472800000810
namely, it is
Figure BDA00025759472800000811
For the battery model corresponding to the battery, the battery can increase the flexibility of the electricity demand of the building through the means of pre-charging, storing the renewable energy surplus power and the like. The battery energy dynamics can be expressed as follows:
Figure BDA00025759472800000812
wherein, bi,a(t) represents the battery energy level in time slot t, bi,a(t-1) represents the battery energy level during time slot t-1,
Figure BDA0002575947280000091
representing the battery charging power during the time slot t,
Figure BDA0002575947280000092
representing the battery discharge power, alpha, in time slot ts∈(0,1]The rate of loss of storage of the battery is expressed,
Figure BDA0002575947280000093
which represents the efficiency of the charging of the battery,
Figure BDA0002575947280000094
represents the battery discharge efficiency, and ηs∈(0,1]。
The constraint formula of the battery model comprises:
Figure BDA0002575947280000095
Figure BDA0002575947280000096
Figure BDA0002575947280000097
wherein, Bi,aThe capacity of the battery is represented by,
Figure BDA0002575947280000098
indicates the minimum charging power of the battery,
Figure BDA0002575947280000099
which represents the maximum charging power of the battery,
Figure BDA00025759472800000910
indicates the minimum discharge power of the battery,
Figure BDA00025759472800000911
representing the maximum discharge power of the battery.
Corresponding microgrid devices for renewable energy models include, but are not limited to: solar photovoltaic power generation and wind turbine power generation are relatively common renewable energy devices. Because the operation condition of the micro-grid equipment is easily influenced by external factors such as weather, for example, solar radiation or wind speed, the power of the micro-grid equipment is often not easy to determine or predict, and the micro-grid equipment is generally considered to be constrained by the maximum output of the equipment, that is, the constraint formula of the renewable energy model corresponding to the micro-grid equipment comprises:
Figure BDA00025759472800000912
moreover, the utility function of the renewable energy model includes:
Ui,a(pi,a(t),t)＝ci,a-bi,api,a(t)
for the above-mentioned predetermined decision model functions, it includes but is not limited to:
Figure BDA00025759472800000913
Figure BDA00025759472800000914
wherein x represents a feasible domain satisfying the operation requirements of each micro-grid device,
Figure 1
representing the prediction period, P (t) representing the total real-time power consumption during the time slot t, PnomAnd (t) the planned power consumption in the day ahead, wherein alpha and beta are coefficients which represent the balance of minimizing the deviation of the actual power consumption and the planned rated power consumption and maximizing the utility of the user in a decision function respectively.
Therefore, the model predictive control is adopted in the method, short-term prediction can be carried out based on real-time weather data and equipment running state parameters, the problem of model predictive control optimization is solved, the system running safety and user comfort maximization is guaranteed while the system requirements are met, and the optimal decision of demand response, energy scheduling and storage scheduling is obtained.
Of course, in another embodiment, the preset decision model function may also be constructed based on the constraint model of the microgrid device and the microgrid system. Of course, in the present embodiment, the construction manner is not limited, and only needs to satisfy the requirements of the present embodiment.
Fig. 2 shows a schematic structural diagram of an electric energy dispatching system for a microgrid according to an embodiment of the present invention. According to fig. 2, another embodiment of the present invention provides an electric energy dispatching system for a microgrid, including:
the acquisition module 201 is used for acquiring historical power distribution information and a power distribution plan of the microgrid device; the optimization module 202 is configured to optimize a preset decision model function through a rolling optimization algorithm for model prediction control based on the historical power distribution information and with a preset time period as a cycle; a solving module 203, configured to solve the optimized preset decision model function; and the scheduling module 204 is used for scheduling the power distribution plan of the microgrid device according to the solution result.
Therefore, the electric energy dispatching system for the micro-grid can construct a load model according to the operation characteristics of different micro-grid equipment in the micro-grid, introduce a rolling optimization algorithm for model prediction control strategy design optimization, design a utility function containing the use preference and the safe operation condition of the micro-grid equipment, and achieve the aims of reducing the peak load in the micro-grid, stabilizing the electric power demand, guaranteeing the operation safety of the power grid and saving the power generation cost through electric energy transaction, dispatching and cooperative management among users.
In another embodiment, one implementation of the solving module 203 includes: the method is specifically used for decomposing the optimized preset decision model function into a power distribution model function corresponding to the microgrid device by using a dual decomposition method; and based on the constraint model of the microgrid equipment, performing distributed solution on the power distribution model function corresponding to the microgrid equipment, and coordinating by using a gradient iterative algorithm to obtain an optimal solution, wherein the optimal solution is the solution result.
In another embodiment, the constraint model of the microgrid device comprises one or more of:
a fixed load model, a dispatchable load model, an interruptible load model, a battery model, or a renewable energy model.
In another embodiment, the constraint of the fixed load model comprises:
Figure BDA0002575947280000101
wherein p isi,a(t) denotes that within a certain time slot t, the building is built
Figure BDA0002575947280000102
Internal device
Figure BDA0002575947280000103
The required power;
Figure BDA0002575947280000104
represents a collection of buildings inside the microgrid,
Figure BDA0002575947280000105
represents a collection of devices inside a building i,
Figure BDA0002575947280000106
indicating the period of time the device is operating.
In another embodiment, the constraint of the schedulable load model comprises:
Figure BDA0002575947280000111
Figure BDA0002575947280000112
wherein the content of the first and second substances,
Figure BDA0002575947280000113
representing the minimum power at which device a operates in a certain time slot t,
Figure BDA0002575947280000114
represents the maximum power at which the device a operates in a certain time slot t,
Figure BDA0002575947280000115
indicating the minimum total energy consumption required by device a during the operating period,
Figure BDA0002575947280000116
representing a runtimeThe maximum total energy consumption required by the device a in the time interval, and delta T represents the time interval between adjacent sampling points;
the utility function of the schedulable load model is represented as:
Figure BDA0002575947280000117
namely:
Figure BDA0002575947280000118
wherein, ci,a、bi,aAll are positive constants and represent utility calculation coefficients of the equipment a.
In another embodiment, the constraint of the interruptible load model comprises:
Figure BDA0002575947280000119
the utility function of the interruptible load model comprises:
Figure BDA00025759472800001110
namely, it is
Figure BDA00025759472800001111
In another embodiment, the dynamic variation of the energy of the battery model comprises:
Figure BDA00025759472800001112
wherein, bi,a(t) represents the battery energy level in time slot t, bi,a(t-1) represents the battery energy level during time slot t-1,
Figure BDA00025759472800001113
representing the battery charging power during the time slot t,
Figure BDA00025759472800001114
representing the battery discharge power, alpha, in time slot ts∈(0,1]The rate of loss of storage of the battery is expressed,
Figure BDA00025759472800001115
which represents the efficiency of the charging of the battery,
Figure BDA00025759472800001116
represents the battery discharge efficiency, and ηs∈(0,1]。
The constraint formula of the battery model comprises:
Figure BDA0002575947280000121
Figure BDA0002575947280000122
Figure BDA0002575947280000123
wherein, Bi,aThe capacity of the battery is represented by,
Figure BDA0002575947280000124
indicates the minimum charging power of the battery,
Figure BDA0002575947280000125
which represents the maximum charging power of the battery,
Figure BDA0002575947280000126
indicates the minimum discharge power of the battery,
Figure BDA0002575947280000127
representing the maximum discharge power of the battery.
In another embodiment, the constraint of the renewable energy model comprises:
Figure BDA0002575947280000128
moreover, the utility function of the renewable energy model includes:
Ui,a(pi,a(t),t)＝ci,a-bi,api,a(t)
in another embodiment, the predetermined decision model function includes:
Figure BDA0002575947280000129
Figure BDA00025759472800001210
wherein x represents a feasible domain satisfying the operation requirements of each micro-grid device,
Figure BDA00025759472800001211
representing the prediction period, P (t) representing the total real-time power consumption during the time slot t, PnomAnd (t) the planned power consumption in the day ahead, wherein alpha and beta are coefficients which represent the balance of minimizing the deviation of the actual power consumption and the planned rated power consumption and maximizing the utility of the user in a decision function respectively.
In another embodiment of the present invention, a computer storage medium is provided, which stores one or more programs that can be executed by one or more processors to implement the above-mentioned method for scheduling electric energy for a microgrid.
The terms and implementation principles related to a storage medium in this embodiment may specifically refer to the electric energy scheduling method for a microgrid in the foregoing embodiment, and are not described herein again.
In another embodiment of the present invention, a central energy management system is provided that includes a processor and a memory; the memory is used for storing computer instructions, and the processor is used for operating the computer instructions stored by the memory, so as to realize the electric energy scheduling method for the micro-grid.
The term and the implementation principle related to the voice command recognition apparatus in this embodiment may specifically refer to the electric energy scheduling method for the microgrid in the above embodiment, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method for scheduling electric energy for a microgrid is characterized by comprising the following steps:
acquiring historical power distribution information and a power distribution plan of the microgrid equipment;
based on the historical power distribution information and the power distribution plan, and with a preset time period as a cycle, optimizing a preset decision model function through a rolling optimization algorithm for model prediction control;
solving the optimized preset decision model function;
and scheduling the power distribution plan of the microgrid equipment according to the solving result.
2. The method of claim 1, wherein solving the optimized predetermined decision model function comprises:
decomposing the optimized preset decision model function into a power distribution model function corresponding to the microgrid equipment by using a dual decomposition method;
and based on the constraint model of the micro-grid equipment, performing distributed solution on the power distribution model function corresponding to the micro-grid equipment, and coordinating by using a gradient iterative algorithm to obtain an optimal solution, wherein the optimal solution is the solution result.
3. The method of claim 2, wherein the constraint model of the microgrid device comprises one or more of:
a fixed load model, a dispatchable load model, an interruptible load model, a battery model, or a renewable energy model.
4. The method of claim 3,
the constraint equation of the fixed load model comprises:
Figure FDA0002575947270000011
wherein p isi,a(t) denotes that within a certain time slot t, the building is built
Figure FDA0002575947270000012
Internal device
Figure FDA0002575947270000013
The required power;
Figure FDA0002575947270000014
represents a collection of buildings inside the microgrid,
Figure FDA0002575947270000015
represents a collection of devices inside a building i,
Figure FDA0002575947270000016
indicating the period of time the device is operating.
5. The method of claim 4, wherein the constraint of the schedulable load model comprises:
Figure FDA0002575947270000017
Figure FDA0002575947270000018
wherein the content of the first and second substances,
Figure FDA0002575947270000019
representing the minimum power at which device a operates in a certain time slot t,
Figure FDA00025759472700000110
represents the maximum power at which the device a operates in a certain time slot t,
Figure FDA0002575947270000021
indicating the minimum total energy consumption required by device a during the operating period,
Figure FDA0002575947270000022
the maximum total energy consumption required by the equipment a in the operation time period is shown, and delta T represents the time interval between adjacent sampling points;
the utility function of the schedulable load model is represented as:
Figure FDA0002575947270000023
namely:
Figure FDA0002575947270000024
wherein, ci,a、bi,aAll are positive constants and represent utility calculation coefficients of the equipment a.
6. The method of claim 5, wherein the constraint of the interruptible load model comprises:
Figure FDA0002575947270000025
the utility function of the interruptible load model comprises:
Figure FDA0002575947270000026
namely, it is
Figure FDA0002575947270000027
7. The method of claim 6, wherein the dynamic variation of the energy of the battery model comprises:
Figure FDA0002575947270000028
wherein, bi,a(t) represents the battery energy level in time slot t, bi,a(t-1) represents the battery energy level during time slot t-1,
Figure FDA0002575947270000029
representing the battery charging power during the time slot t,
Figure FDA00025759472700000210
representing the battery discharge power, alpha, in time slot ts∈(0,1]The rate of loss of storage of the battery is expressed,
Figure FDA00025759472700000211
which represents the efficiency of the charging of the battery,
Figure FDA00025759472700000212
represents the battery discharge efficiency, and ηs∈(0,1]。
The constraint formula of the battery model comprises:
Figure FDA00025759472700000213
Figure FDA00025759472700000214
Figure FDA00025759472700000215
wherein, Bi,aThe capacity of the battery is represented by,
Figure FDA00025759472700000216
indicates the minimum charging power of the battery,
Figure FDA00025759472700000217
which represents the maximum charging power of the battery,
Figure FDA0002575947270000031
indicates the minimum discharge power of the battery,
Figure FDA0002575947270000032
representing the maximum discharge power of the battery.
8. The method of claim 7, wherein the constraint of the renewable energy model comprises:
Figure FDA0002575947270000033
moreover, the utility function of the renewable energy model includes:
Ui,a(pi,a(t),t)＝ci,a-bi,api,a(t) 。
9. the method of claim 8, wherein the predetermined decision model function comprises:
Figure FDA0002575947270000034
Figure FDA0002575947270000035
wherein the content of the first and second substances,
Figure FDA0002575947270000036
representing a feasible domain that meets the operational requirements of the individual microgrid devices,
Figure FDA0002575947270000037
representing the prediction period, P (t) representing the time slot within tTotal power consumption in hours, PnomAnd (t) the planned power consumption in the day ahead, wherein alpha and beta are coefficients which represent the balance of minimizing the deviation of the actual power consumption and the planned rated power consumption and maximizing the utility of the user in a decision function respectively.
10. An electric energy dispatching system for a microgrid, the system comprising:
the acquisition module is used for acquiring historical power distribution information and a power distribution plan of the microgrid equipment;
the optimization module is used for optimizing a preset decision model function through a rolling optimization algorithm for model prediction control based on the historical power distribution information and with a preset time period as a cycle;
the solving module is used for solving the optimized preset decision model function;
and the scheduling module is used for scheduling the power distribution plan of the micro-grid equipment according to the solving result.
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